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Convolutional encoder-decoder networks for pixel-wise ear detection and segmentation
Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, SL-1000 Ljubljana, Slovenia..
KTH.
Univ Ljubljana, Fac Elect Engn, Trzaska 25, SL-1000 Ljubljana, Slovenia..
Univ Ljubljana, Fac Comp & Informat Sci, Vecna Pot 113, SL-1000 Ljubljana, Slovenia..
2018 (English)In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 7, no 3, p. 175-184Article in journal (Refereed) Published
Abstract [en]

Object detection and segmentation represents the basis for many tasks in computer and machine vision. In biometric recognition systems the detection of the region-of-interest (ROI) is one of the most crucial steps in the processing pipeline, significantly impacting the performance of the entire recognition system. Existing approaches to ear detection, are commonly susceptible to the presence of severe occlusions, ear accessories or variable illumination conditions and often deteriorate in their performance if applied on ear images captured in unconstrained settings. To address these shortcomings, we present a novel ear detection technique based on convolutional encoder-decoder networks (CEDs). We formulate the problem of ear detection as a two-class segmentation problem and design and train a CED-network architecture to distinguish between image-pixels belonging to the ear and the non-ear class. Unlike competing techniques, our approach does not simply return a bounding box around the detected ear, but provides detailed, pixel-wise information about the location of the ears in the image. Experiments on a dataset gathered from the web (a.k.a. in the wild) show that the proposed technique ensures good detection results in the presence of various covariate factors and significantly outperforms competing methods from the literature.

Place, publisher, year, edition, pages
INST ENGINEERING TECHNOLOGY-IET , 2018. Vol. 7, no 3, p. 175-184
Keywords [en]
object detection, computer vision, biometrics (access control), feature extraction, image segmentation, ear, convolutional encoder-decoder, pixel-wise ear detection, machine vision, biometric recognition systems, entire recognition system, ear accessories, ear images, ear detection technique, two-class segmentation problem, design, image-pixels, nonear class, detected ear, pixel-wise information, good detection results
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-227756DOI: 10.1049/iet-bmt.2017.0240ISI: 000430322000002Scopus ID: 2-s2.0-85045624452OAI: oai:DiVA.org:kth-227756DiVA, id: diva2:1205684
Conference
Conference on Unconstrained Ear Recognition Challenge (UERC) held in conjunction with the International Joint Conference on Biometrics (IJCB), OCT 01-04, 2017, Denver, CO
Note

QC 20180515

Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

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